diff --git a/31-plotting_sample_size_biological_context_coverage.Rmd b/31-plotting_sample_size_biological_context_coverage.Rmd
index 124aae3..b70275a 100644
--- a/31-plotting_sample_size_biological_context_coverage.Rmd
+++ b/31-plotting_sample_size_biological_context_coverage.Rmd
@@ -169,14 +169,16 @@ will be plotting are from `30-evaluate_sample_size_and_biological_context`.
# pathway coverage and proportion of LVs associated with pathways
context.coverage.file <- file.path("results", "30",
"biological_context_pathway_coverage.tsv")
+
+# we'll want to reorder the biological contexts by sample size
+context.levels <- c("blood", "cancer", "tissue", "cell line", "other tissues")
+
context.coverage.df <- readr::read_tsv(context.coverage.file) %>%
# clean up the context information for plotting
dplyr::mutate(biological_context = gsub("_", " ", biological_context)) %>%
# reorder by the sample size (low -> high)
dplyr::mutate(biological_context = factor(biological_context,
- levels = c("blood", "cancer",
- "tissue", "cell line",
- "other tissues")))
+ levels = context.levels))
# number of latent variables
context.num.file <- file.path("results", "30",
"biological_context_number_of_lvs.tsv")
@@ -185,9 +187,7 @@ context.num.df <- readr::read_tsv(context.num.file) %>%
dplyr::mutate(biological_context = gsub("_", " ", biological_context)) %>%
# reorder by the sample size (low -> high)
dplyr::mutate(biological_context = factor(biological_context,
- levels = c("blood", "cancer",
- "tissue", "cell line",
- "other tissues")))
+ levels = context.levels))
```
### Sample size
@@ -196,21 +196,21 @@ context.num.df <- readr::read_tsv(context.num.file) %>%
# pathway coverage and proportion of LVs associated with pathways
size.coverage.file <- file.path("results", "30",
"subsampled_pathway_coverage.tsv")
+
+# order by sample size
+size.levels <- c("500", "1000", "2000", "4000", "8000", "16000", "32000")
+
# for plotting, we want the sample size as a factor (otherwise it's hard to
# see what is happening on the lower end of things) but to order by the integer
# value
size.coverage.df <- readr::read_tsv(size.coverage.file) %>%
- dplyr::mutate(sample_size = factor(sample_size,
- levels = c("500", "1000", "2000", "4000",
- "8000", "16000", "32000")))
+ dplyr::mutate(sample_size = factor(sample_size, levels = size.levels))
# Number of latent variables
size.num.file <- file.path("results", "30",
"subsampled_number_of_lvs.tsv")
size.num.df <- readr::read_tsv(size.num.file) %>%
- dplyr::mutate(sample_size = factor(sample_size,
- levels = c("500", "1000", "2000", "4000",
- "8000", "16000", "32000")))
+ dplyr::mutate(sample_size = factor(sample_size, levels = size.levels))
```
### MultiPLIER
diff --git a/31-plotting_sample_size_biological_context_coverage.nb.html b/31-plotting_sample_size_biological_context_coverage.nb.html
index a10ec50..0b8bd98 100644
--- a/31-plotting_sample_size_biological_context_coverage.nb.html
+++ b/31-plotting_sample_size_biological_context_coverage.nb.html
@@ -443,18 +443,18 @@ Read in data
Biological context
-
+
# pathway coverage and proportion of LVs associated with pathways
context.coverage.file <- file.path("results", "30",
"biological_context_pathway_coverage.tsv")
+# we'll want to reorder the biological contexts by sample size
+context.levels <- c("blood", "cancer", "tissue", "cell line", "other tissues")
context.coverage.df <- readr::read_tsv(context.coverage.file) %>%
# clean up the context information for plotting
dplyr::mutate(biological_context = gsub("_", " ", biological_context)) %>%
# reorder by the sample size (low -> high)
dplyr::mutate(biological_context = factor(biological_context,
- levels = c("blood", "cancer",
- "tissue", "cell line",
- "other tissues")))
+ levels = context.levels))
Parsed with column specification:
@@ -465,7 +465,7 @@ Biological context
biological_context = col_character()
)
-
+
# number of latent variables
context.num.file <- file.path("results", "30",
"biological_context_number_of_lvs.tsv")
@@ -474,9 +474,7 @@ Biological context
dplyr::mutate(biological_context = gsub("_", " ", biological_context)) %>%
# reorder by the sample size (low -> high)
dplyr::mutate(biological_context = factor(biological_context,
- levels = c("blood", "cancer",
- "tissue", "cell line",
- "other tissues")))
+ levels = context.levels))
Parsed with column specification:
@@ -493,17 +491,17 @@ Biological context
Sample size
-
+
# pathway coverage and proportion of LVs associated with pathways
size.coverage.file <- file.path("results", "30",
"subsampled_pathway_coverage.tsv")
+# order by sample size
+size.levels <- c("500", "1000", "2000", "4000", "8000", "16000", "32000")
# for plotting, we want the sample size as a factor (otherwise it's hard to
# see what is happening on the lower end of things) but to order by the integer
# value
size.coverage.df <- readr::read_tsv(size.coverage.file) %>%
- dplyr::mutate(sample_size = factor(sample_size,
- levels = c("500", "1000", "2000", "4000",
- "8000", "16000", "32000")))
+ dplyr::mutate(sample_size = factor(sample_size, levels = size.levels))
Parsed with column specification:
@@ -514,14 +512,12 @@ Sample size
sample_size = col_integer()
)
-
+
# Number of latent variables
size.num.file <- file.path("results", "30",
"subsampled_number_of_lvs.tsv")
size.num.df <- readr::read_tsv(size.num.file) %>%
- dplyr::mutate(sample_size = factor(sample_size,
- levels = c("500", "1000", "2000", "4000",
- "8000", "16000", "32000")))
+ dplyr::mutate(sample_size = factor(sample_size, levels = size.levels))
Parsed with column specification:
@@ -666,13 +662,13 @@ Following up on the proportion of LVs associated with pathways results
prop.plots$size
-
+
prop.plots$context
-
+
prop.plots$recount2
@@ -825,7 +821,7 @@ Check sparsity
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diff --git a/plots/31/lv_proportion.pdf b/plots/31/lv_proportion.pdf
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diff --git a/plots/31/number_of_latent_variables.pdf b/plots/31/number_of_latent_variables.pdf
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diff --git a/plots/31/pathway_coverage.pdf b/plots/31/pathway_coverage.pdf
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